Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.

The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
A . No; Delta Lake manages streaming checkpoints in the transaction log.
B . Yes; both of the streams can share a single checkpoint directory.
C . No; only one stream can write to a Delta Lake table.
D . Yes; Delta Lake supports infinite concurrent writers.
E . No; each of the streams needs to have its own checkpoint directory.

Answer: E

Explanation:

This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss.

Verified Reference: [Databricks Certified Data Engineer Professional], under “Structured Streaming” section; Databricks Documentation, under “Checkpointing” section.